The article introduces RSeQC, a comprehensive quality control (QC) tool for RNA-seq experiments. RSeQC is designed to address the limitations of existing QC tools by providing a more extensive and efficient evaluation of RNA-seq data. It assesses various aspects of RNA-seq experiments, including sequence quality, GC bias, PCR bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity, and read distribution over the genome structure. RSeQC supports both SAM and BAM files, making it versatile for different RNA-seq mapping tools. The tool is implemented in Python and C, and its source code and user manual are freely available online. RSeQC includes several modules for specific tasks, such as checking mapping statistics, estimating inner distance distribution, calculating coverage profiles, and determining the precision of estimated RPKMs. The article highlights the importance of these features in ensuring the accuracy and reliability of RNA-seq data, particularly in clinical and model organism studies.The article introduces RSeQC, a comprehensive quality control (QC) tool for RNA-seq experiments. RSeQC is designed to address the limitations of existing QC tools by providing a more extensive and efficient evaluation of RNA-seq data. It assesses various aspects of RNA-seq experiments, including sequence quality, GC bias, PCR bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity, and read distribution over the genome structure. RSeQC supports both SAM and BAM files, making it versatile for different RNA-seq mapping tools. The tool is implemented in Python and C, and its source code and user manual are freely available online. RSeQC includes several modules for specific tasks, such as checking mapping statistics, estimating inner distance distribution, calculating coverage profiles, and determining the precision of estimated RPKMs. The article highlights the importance of these features in ensuring the accuracy and reliability of RNA-seq data, particularly in clinical and model organism studies.